Abstract
With the improvement of social informatization and the popularization of Internet of Things devices, the scale, complexity and diversity of data are currently growing rapidly, and traditional storage solutions have been unable to meet the complex and diverse applications and large-scale new storage requirements. Existing storage solutions still have deficiencies in data compression and adapting to the diversity of system architectures, resulting in a large waste of storage space resources, which in turn increases the total cost of ownership of platform data. Therefore, this paper will study the data compression strategy of database file storage, and propose a high-relevance mode access data compression method. The data request of the write-only instance of the database hosted on the cloud platform is aggregated with the system workload. The data stored in the write-only instance is compressed, which improves data storage efficiency and storage space utilization. The method was validated using data in real enterprise scenarios. The experimental results show that the proposed method has a certain degree of improvement in storage space utilization compared with the original method.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Babar, M., Arif, F.: Real-time data processing scheme using big data analytics in internet of things based smart transportation environment. J. Ambient. Intell. Humaniz. Comput. 10(10), 4167–4177 (2018). https://doi.org/10.1007/s12652-018-0820-5
Farooq, U., Ryoo, I., Khang, G.: A smart wellness service platform and its practical implementation. Comput. Mater. Continua 66(1), 45–57 (2021)
Khan, A., et al.: Intelligent cloud based load balancing system empowered with fuzzy logic. Comput. Mater. Continua 67, 519–528 (2021)
Hoon Kim, T., Ramos, C., Mohammed, S.: Smart city and IOT. Futur. Gener. Comput. Syst. 76, 159–162 (2017)
Mao, B., Wu, S., Jiang, H., Yang, Y., **, Z.: Edc: Improving the performance and space efficiency of flash-based storage systems with elastic data compression. IEEE Trans. Parallel Distrib. Syst. 29(6), 1261–1274 (2018)
Marikyan, D., Papagiannidis, S., Alamanos, E.: A systematic review of the smart home literature: a user perspective. Technol. Forecast. Soc. Chang. 138, 139–154 (2019)
Mehmood, F., Ahmad, S., Ullah, I., Jamil, F., Kim, D.: Towards a dynamic virtual IOT network based on user requirements. Comput. Mater. Continua 69, 2231–2244 (2021)
Sahu, P., Singh, D., Singh, A.: Blockchain based secure solution for cloud storage: a model for synchronizing industry 4.0 and IOT. J. Cyber Security 3, 107–115 (2021)
Son, Y., et al.: Ssd-assisted backup and recovery for database systems. In: 2017 IEEE 33rd International Conference on Data Engineering, ICDE, pp. 285–296 (2017)
Sridharan, M., Murugaiyan, C.: Virtualized load balancer for hybrid cloud using genetic algorithm. Intell. Autom. Soft Comput. 32, 1459–1466 (2021)
Takruri, H., Kettaneh, I., Alquraan, A., Al-Kiswany, S.: FLAIR: accelerating reads with consistency-aware network routing. In: 17th USENIX Symposium on Networked Systems Design and Implementation, NSDI 20, pp. 723–737. USENIX Association, Santa Clara, Febuary 2020
Wu, S., Yi, Y., **ao, J., **, H., Ye, M.: A large-scale study of i/o workload’s impact on disk failure. IEEE Access 6, 47385–47396 (2018)
Yang, J., Yue, Y., Rashmi, K.V.: A large scale analysis of hundreds of in-memory cache clusters at twitter. In: 14th USENIX Symposium on Operating Systems Design and Implementation, OSDI 2020, pp. 191–208. USENIX Association, November 2020
Acknowledgement
The authors would like to thank the associate editor and the reviewers for their time and effort provided to review the manuscript.
Funding
This work is supported by the Fundamental Research Funds for the Central Universities (Grant No. HIT. NSRIF. 201714), Weihai Science and Technology Development Program (2016DX GJMS15), Future Network Scientific Research Fund Project (SN: FNSRFP-2021-YB-56) and Key Research and Development Program in Shandong Provincial (2017GGX90103).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Ethics declarations
Conflicts of Interest
The authors declare that they have no conflicts of interest to report regarding the present study.
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Gao, R. et al. (2022). A Database File Storage Optimization Strategy Based on High-Relevance Mode Access Data Compression. In: Sun, X., Zhang, X., **a, Z., Bertino, E. (eds) Advances in Artificial Intelligence and Security. ICAIS 2022. Communications in Computer and Information Science, vol 1587. Springer, Cham. https://doi.org/10.1007/978-3-031-06761-7_29
Download citation
DOI: https://doi.org/10.1007/978-3-031-06761-7_29
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-06760-0
Online ISBN: 978-3-031-06761-7
eBook Packages: Computer ScienceComputer Science (R0)